Because manual discrete and continuous emotion annotation is a highly subjective perception task, it has to be done by multiple annotators to be relevant, and this explains the huge cost of the creation of large emotional speech datasets. Consequently, Speech Emotion Recognition (SER) databases are usually quite small (SEWA [1]}: \(\sim \) 44 h, RECOLA [2]}: \(\sim \) 2.8 h, AlloSat [3]}: \(\sim \) 37 h).
That is one of the reasons why deep neural networks (DNN) have been used only recently in SER in comparison to automatic speech recognition (ASR) where accessible databases are drastically bigger (e.g. TED-LIUM 3 [4]}: \(\sim \) 450 h, LibriSpeech [5]}: \(\sim \) 960 h).
